Chuang-Wen You

Impact of sensor-enhanced mobility prediction on the design of energy-efficient localization

Energy efficiency and positional accuracy are often contradictive goals. We propose to decrease power consumption without sacrificing significant accuracy by developing an energy-aware localization that adapts the sampling rate to targets mobility level. In this paper, an energy-aware adaptive localization system based on signal strength fingerprinting is designed, implemented, and evaluated. Promising to satisfy an applications requirements on positional accuracy, our system tries to adapt its sampling rate to reduce its energy consumption. The contribution of this paper is fourfold. (1) We have developed a model to predict the positional error of a real working positioning engine under different mobility levels of mobile targets, estimation error from the positioning engine, processing and networking delay in the location infrastructure, and sampling rate of location information. (2) In a real test environment, our energy-saving method solves the mobility estimation error problem by utilizing additional sensors on mobile targets. The result is that we can improve the prediction accuracy by 56.34% on average, comparing to algorithms without utilizing additional sensors. (3) We further enhance our sensor-enhanced mobility prediction algorithm by detecting the targets moving foot step and then estimate the targets velocity. This method can improve the mobility prediction accuracy by 49.81% on an average, comparing to previous sensor-enhanced mobility prediction algorithm. (4) We implemented our energy-saving methods inside a working localization infrastructure and conducted performance evaluation in a real office environment. Our performance results show as much as 68.92% reduction in power consumption.

Sensor-Enhanced Mobility Prediction for Energy-Efficient Localization

Energy efficiency and positional accuracy are often contradictive goals. We propose to decrease power consumption without sacrificing significant accuracy by developing an energy-aware localization that adapts the sampling rate to targets mobility level. In this paper, an energy-aware adaptive localization system based on signal strength fingerprinting is designed, implemented, and evaluated. Promising to satisfy an applications requirements on positional accuracy, our system tries to adapt its sampling rate to reduce its energy consumption. The contribution of this paper is three-fold. (1) We have developed a model to predict the positional error of a real working positioning engine under different mobility levels of mobile targets, estimation error from the positioning engine, processing and networking delay in the location infrastructure, and sampling rate of location information. (2) In a real test environment, our energy-saving method solves the mobility estimation error problem by utilizing additional sensors on mobile targets. The result is that we can improve the prediction accuracy by as much as 37.01%. (3) We implemented our energy-saving methods inside a working localization infrastructure and conducted performance evaluation in a real office environment. Our performance results show as much as 49.76 % reduction in power consumption

Challenges: wireless Web services

This paper describes the challenges of adapting existing Web-service architecture to the wireless environment. It presents a new, wireless, Web-service architecture based on the smart client model that can address some of the fundamental differences between the wireless and wireline environments. The fundamental differences between these environments can be called the mobile challenges, including (1) the unpredictable nature of the wireless network, (2) the limited processing capabilities and power on mobile devices, and (3) the need for consumer device to have a fast startup time for mobile applications. The smart client model suggests the following modifications to the existing Web-service stack in order to overcome these mobile challenges: (1) a lightweight Web-service container that runs on a resource-limited mobile device (the client), (2) a quality of user experience (QoUE) model based on application response time, application startup time, and power consumption, and (3) an adaptive application configuration algorithm that can exploit the tradeoff among the QoUE parameters to provide the best user experience given preferences and the device platform.

Energy-Efficient Boundary Detection for RF-Based Localization Systems

Boundary detection is a form of location-aware services that aims at detecting targets crossing certain critical regions. Typically, a lower location sampling rate contributes to a lower level of energy consumption but, in the meantime, delays the detection of boundary crossing events. Opting to enable energy-efficient boundary detection services, we propose a mobility-aware mechanism that adapts the location sampling rate to the target mobility. Results from our simulations and live experiments confirm that the proposed adaptive sampling mechanism is effective. In particular, when experimented with realistic errors measured from a live radio-frequency-based localization system, the energy consumption can be reduced significantly to 20 percent.

A mobile mediation tool for improving interaction between depressed individuals and caregivers

Depression, a common mental disorder, significantly affects an individual’s ability to live a normal life. Excessive reassurance-seeking is a common interpersonal behavior characteristic of depression and often leads to negative interaction between caregivers and depressed individuals, who seek excessive assurance from the caregivers at inappropriate times, such as when caregivers are busy. Such negative interaction results in elevated feelings of burden for both caregivers and care receivers. However, maintaining a good relationship between depressed individuals and caregivers is crucial to overcoming difficult times during depression. Therefore, we propose a mobile care mediation system that enables depressed individuals and caregivers to share mood and availability information, thus improving communication in caregiving and care-receiving and reducing the caregiving burden.

Modeling and optimizing positional accuracy based on hyperbolic geometry for the adaptive radio interferometric positioning system

One of the most important performance objectives for a localization system is positional accuracy. It is fundamental and essential to general location-aware services. The radio interferometric positioning (RIP) method [1] is an exciting approach which promises sub-meter positional accuracy. In this work, we would like to enhance the RIP method by dynamically selecting the optimal anchor nodes as beacon senders to further optimizing the positional accuracy when tracking targets. We have developed an estimation error model to predict positional error of the RIP algorithm given different combinations of beacon senders. Building upon this estimation error model, we further devise an adaptive RIP method that selects the optimal sender-pair combination (SPC) according to the locations of targets relative to anchor nodes. We have implemented the adaptive RIP method and conducted experiments in a real sensor network testbed. Experimental results have shown that our adaptive RIP method outperforms the static RIP method in both single-target and multi-target tracking, and improves the average positional accuracy by 47%-60% and reduces the 90% percentile error by 55%-61%.

BioScope: an extensible bandage system for facilitating data collection in nursing assessments

To facilitate the collection of patient biosignals, designing extensible sensing devices in which sensor management is simplified is essential. This paper presents BioScope, an extensible sensing system that facilitates collecting data used in nursing assessments. We conducted experiments to demonstrate the potential of the system. The results obtained in this study can be applied in improving the design, thus enabling BioScope to facilitate data collection in numerous potential applications.

KeDiary: Using Mobile Phones to Assist Patients in Recovering from Drug Addiction

Ketamine is an addictive drug that has been shown to inflict considerable physical and mental damage on users. Due in part to its low cost, ketamine has become one of the most popular club drugs among young adults and teenagers in Southeast Asia. This paper proposes a phone-based support system (KeDiary) with Bluetooth-enabled device for the screening of saliva, as a means of assisting ketamine-dependent patients to self-monitor their ketamine use following acute withdrawal treatment. We also conducted a practical experiment to evaluate the feasibility of the proposed system, wherein three ketamine-dependent patients self-administered tests at least once per day over a period of three weeks. Follow-up interviews with the same users helped in the further refinement of the proposed self-monitoring system.

ThermalProbe: Exploring the Use of Thermal Identification for Per-User Energy Metering

Given the strong link between energy and behavior, sensing and metering per-user energy consumption is critical for understanding individual energy behavior and for customizing personalized feedback to promote energy-saving behavior. This paper explores the feasibility of per-user energy metering by proposing a per-user energy metering system that uses thermal-imaging and thermal-identification to track and associate energy usage among individual occupants in a shared working/living space. Each occupant wears a thermal tag that emits a unique temperature signature for user identification. The system introduces location-based per-user energy disaggregation that accounts per-appliance energy usage to individual energy consumer (s), i.e., Occupant (s) nearby activated appliances. We have designed, prototyped, and tested the Thermal Probe system. Results show that the system meters per-user energy consumption with an average error of 12.66%.

A mobile support system to assist DUI offenders on probation in reducing DUI relapse

This paper proposes a mobile support system to assist DUI (Driving Under the Influence of Alcohol) offenders on probation in avoiding committing DUI again. A customizable portable breathalyzer is used in conjunction with the DUI offenders mobile phone to self-administer alcohol screening tests and send the results to a server. The system also transmits contextual information and momentary feedback to the server. Records pertaining to alcohol use are summarized on a server and a list of triggering events are inferred from the information sent from the phone. This data can then be used to characterize the difficulties faced by these individuals, monitor their compliance with their probation requirements, and gauge their progress.